Price: $14.43
(as of Dec 24,2024 08:11:05 UTC – Details)
ASIN : B0B2PTF5PC
Publisher : Packt Publishing; 1st edition (July 29, 2022)
Publication date : July 29, 2022
Language : English
File size : 18121 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 304 pages
In the world of machine learning, one of the biggest challenges that researchers and practitioners face is the lack of transparency and interpretability of models. This is especially important in practical applications where decisions made by machine learning models can have significant real-world consequences.
One way to address this issue is through the use of explainability techniques, which aim to make machine learning models more interpretable and trustworthy. Some popular techniques for explainability include Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP).
LIME is a technique that can explain the predictions of any machine learning model by approximating it with a simpler, more interpretable model that is locally faithful to the original model. This allows users to understand why a model made a particular prediction for a specific instance, making the model more transparent and trustworthy.
On the other hand, SHAP is a unified approach to explain the output of any machine learning model. It assigns each feature an importance value for a particular prediction, providing a global view of how each feature contributes to the model’s output. This can help users understand the overall behavior of the model and identify potential biases or errors.
By incorporating these explainability techniques into machine learning models, researchers and practitioners can make their models more transparent, interpretable, and trustworthy for practical applications. This not only helps build trust with stakeholders and end-users but also enables better decision-making and problem-solving in real-world scenarios.
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